Overview

Dataset statistics

Number of variables19
Number of observations47511
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.9 MiB
Average record size in memory152.0 B

Variable types

Numeric16
Categorical3

Warnings

df_index is highly correlated with track_hash and 2 other fieldsHigh correlation
track_hash is highly correlated with df_index and 1 other fieldsHigh correlation
popularity is highly correlated with df_index and 1 other fieldsHigh correlation
acousticness is highly correlated with energy and 1 other fieldsHigh correlation
energy is highly correlated with acousticness and 1 other fieldsHigh correlation
instrumentalness is highly correlated with loudnessHigh correlation
loudness is highly correlated with acousticness and 2 other fieldsHigh correlation
music_genre is highly correlated with df_index and 2 other fieldsHigh correlation
df_index is highly correlated with track_hash and 2 other fieldsHigh correlation
track_hash is highly correlated with df_index and 1 other fieldsHigh correlation
popularity is highly correlated with df_index and 1 other fieldsHigh correlation
acousticness is highly correlated with energy and 1 other fieldsHigh correlation
energy is highly correlated with acousticness and 1 other fieldsHigh correlation
loudness is highly correlated with acousticness and 1 other fieldsHigh correlation
music_genre is highly correlated with df_index and 2 other fieldsHigh correlation
df_index is highly correlated with music_genreHigh correlation
acousticness is highly correlated with energyHigh correlation
energy is highly correlated with acousticness and 1 other fieldsHigh correlation
loudness is highly correlated with energyHigh correlation
music_genre is highly correlated with df_indexHigh correlation
valence is highly correlated with energy and 1 other fieldsHigh correlation
energy is highly correlated with valence and 6 other fieldsHigh correlation
speechiness is highly correlated with df_indexHigh correlation
popularity is highly correlated with df_index and 2 other fieldsHigh correlation
df_index is highly correlated with energy and 8 other fieldsHigh correlation
music_genre is highly correlated with energy and 6 other fieldsHigh correlation
acousticness is highly correlated with energy and 3 other fieldsHigh correlation
instrumentalness is highly correlated with df_index and 1 other fieldsHigh correlation
track_hash is highly correlated with energy and 3 other fieldsHigh correlation
danceability is highly correlated with valence and 4 other fieldsHigh correlation
loudness is highly correlated with energy and 5 other fieldsHigh correlation
df_index is uniformly distributed Uniform
track_hash is uniformly distributed Uniform
df_index has unique values Unique

Reproduction

Analysis started2021-09-09 08:19:38.761857
Analysis finished2021-09-09 08:20:47.271514
Duration1 minute and 8.51 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct47511
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25024.83012
Minimum0
Maximum49999
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size371.3 KiB
2021-09-09T16:20:47.412516image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2510.5
Q112559.5
median25044
Q337498.5
95-th percentile47496.5
Maximum49999
Range49999
Interquartile range (IQR)24939

Descriptive statistics

Standard deviation14419.58228
Coefficient of variation (CV)0.5762109956
Kurtosis-1.19704521
Mean25024.83012
Median Absolute Deviation (MAD)12469
Skewness-0.00271137893
Sum1188954704
Variance207924353.1
MonotonicityStrictly increasing
2021-09-09T16:20:47.606512image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
498691
 
< 0.1%
211831
 
< 0.1%
437121
 
< 0.1%
416651
 
< 0.1%
478101
 
< 0.1%
457631
 
< 0.1%
355241
 
< 0.1%
334771
 
< 0.1%
396221
 
< 0.1%
Other values (47501)47501
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
91
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
499991
< 0.1%
499981
< 0.1%
499971
< 0.1%
499961
< 0.1%
499951
< 0.1%
499941
< 0.1%
499931
< 0.1%
499921
< 0.1%
499911
< 0.1%
499901
< 0.1%

artist_name
Real number (ℝ)

Distinct6862
Distinct (%)14.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.076783839 × 10-16
Minimum-1.721086277
Maximum1.729506856
Zeros0
Zeros (%)0.0%
Negative24057
Negative (%)50.6%
Memory size371.3 KiB
2021-09-09T16:20:47.797514image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1.721086277
5-th percentile-1.552605196
Q1-0.8253704382
median-0.0307432481
Q30.8423408038
95-th percentile1.588686848
Maximum1.729506856
Range3.450593134
Interquartile range (IQR)1.667711242

Descriptive statistics

Standard deviation1.000010524
Coefficient of variation (CV)-9.287012749 × 1015
Kurtosis-1.169007965
Mean-1.076783839 × 10-16
Median Absolute Deviation (MAD)0.838884907
Skewness0.0359342833
Sum-5.115907697 × 10-12
Variance1.000021048
MonotonicityNot monotonic
2021-09-09T16:20:48.000513image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4430154943429
 
0.9%
1.588686848402
 
0.8%
0.1146031177317
 
0.7%
-0.2510259755314
 
0.7%
-0.6644332858241
 
0.5%
-0.8746574411172
 
0.4%
-1.217151819169
 
0.4%
1.637973851167
 
0.4%
-0.8022357225147
 
0.3%
-0.6583981425124
 
0.3%
Other values (6852)45029
94.8%
ValueCountFrequency (%)
-1.72108627722
 
< 0.1%
-1.7205833491
 
< 0.1%
-1.720080423
 
< 0.1%
-1.7195774911
 
< 0.1%
-1.71907456392
0.2%
-1.7185716342
 
< 0.1%
-1.7180687061
 
< 0.1%
-1.7175657775
 
< 0.1%
-1.7170628487
 
< 0.1%
-1.716559922
 
< 0.1%
ValueCountFrequency (%)
1.72950685610
< 0.1%
1.7290039281
 
< 0.1%
1.7285009991
 
< 0.1%
1.7279980712
 
< 0.1%
1.7274951421
 
< 0.1%
1.7269922131
 
< 0.1%
1.7264892851
 
< 0.1%
1.7259863564
 
< 0.1%
1.7254834288
< 0.1%
1.7249804994
 
< 0.1%

track_hash
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct41248
Distinct (%)86.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.148569428 × 10-16
Minimum-1.729783538
Maximum1.730940765
Zeros0
Zeros (%)0.0%
Negative23776
Negative (%)50.0%
Memory size371.3 KiB
2021-09-09T16:20:48.222548image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1.729783538
5-th percentile-1.558161079
Q1-0.8675600235
median-0.001309191481
Q30.8651094454
95-th percentile1.558563184
Maximum1.730940765
Range3.460724303
Interquartile range (IQR)1.732669469

Descriptive statistics

Standard deviation1.000010524
Coefficient of variation (CV)8.706574452 × 1015
Kurtosis-1.201360223
Mean1.148569428 × 10-16
Median Absolute Deviation (MAD)0.8663766857
Skewness-0.0007803480472
Sum5.456968211 × 10-12
Variance1.000021048
MonotonicityNot monotonic
2021-09-09T16:20:48.419515image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.93042750176
 
< 0.1%
-0.71473171316
 
< 0.1%
1.6548412445
 
< 0.1%
-0.58141072215
 
< 0.1%
0.87068895825
 
< 0.1%
0.95853482215
 
< 0.1%
0.61285673335
 
< 0.1%
0.39387134215
 
< 0.1%
0.87278651955
 
< 0.1%
1.1322128915
 
< 0.1%
Other values (41238)47459
99.9%
ValueCountFrequency (%)
-1.7297835381
< 0.1%
-1.7296996351
< 0.1%
-1.7296157331
< 0.1%
-1.7295318311
< 0.1%
-1.7294479281
< 0.1%
-1.7293640261
< 0.1%
-1.7292801231
< 0.1%
-1.7291962211
< 0.1%
-1.7291123181
< 0.1%
-1.7290284161
< 0.1%
ValueCountFrequency (%)
1.7309407651
< 0.1%
1.7308568632
< 0.1%
1.730772961
< 0.1%
1.7306890581
< 0.1%
1.7306051551
< 0.1%
1.7305212531
< 0.1%
1.730437352
< 0.1%
1.7303534481
< 0.1%
1.7302695461
< 0.1%
1.7301856431
< 0.1%

track_name
Real number (ℝ)

Distinct39917
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.459640315 × 10-16
Minimum-1.731675933
Maximum1.757221333
Zeros0
Zeros (%)0.0%
Negative23840
Negative (%)50.2%
Memory size371.3 KiB
2021-09-09T16:20:48.633512image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1.731675933
5-th percentile-1.551925526
Q1-0.8615056571
median-0.006106992519
Q30.8603922321
95-th percentile1.564884465
Maximum1.757221333
Range3.488897266
Interquartile range (IQR)1.721897889

Descriptive statistics

Standard deviation1.000010524
Coefficient of variation (CV)6.851074979 × 1015
Kurtosis-1.193975662
Mean1.459640315 × 10-16
Median Absolute Deviation (MAD)0.8611237566
Skewness0.01351652697
Sum6.934897101 × 10-12
Variance1.000021048
MonotonicityNot monotonic
2021-09-09T16:20:48.819548image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.509740273616
 
< 0.1%
-0.760770260314
 
< 0.1%
1.41441506313
 
< 0.1%
-0.798529645512
 
< 0.1%
1.54744697112
 
< 0.1%
-0.953063425612
 
< 0.1%
-0.921684677312
 
< 0.1%
0.963837214411
 
< 0.1%
0.800475429911
 
< 0.1%
-0.453101195810
 
< 0.1%
Other values (39907)47388
99.7%
ValueCountFrequency (%)
-1.7316759331
< 0.1%
-1.7315885271
< 0.1%
-1.7315011211
< 0.1%
-1.7314137151
< 0.1%
-1.7313263091
< 0.1%
-1.7312389031
< 0.1%
-1.7311514971
< 0.1%
-1.7310640911
< 0.1%
-1.7309766851
< 0.1%
-1.7308892791
< 0.1%
ValueCountFrequency (%)
1.7572213331
< 0.1%
1.7571339271
< 0.1%
1.7570465211
< 0.1%
1.7569591151
< 0.1%
1.7568717091
< 0.1%
1.7567843031
< 0.1%
1.7566968971
< 0.1%
1.7566094911
< 0.1%
1.7565220851
< 0.1%
1.7564346791
< 0.1%

popularity
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct99
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.105710618 × 10-16
Minimum-2.844925579
Maximum3.525124643
Zeros0
Zeros (%)0.0%
Negative23373
Negative (%)49.2%
Memory size371.3 KiB
2021-09-09T16:20:49.010547image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2.844925579
5-th percentile-1.68673463
Q1-0.6572315635
median0.05055179453
Q30.7583351526
95-th percentile1.530462452
Maximum3.525124643
Range6.370050223
Interquartile range (IQR)1.415566716

Descriptive statistics

Standard deviation1.000010524
Coefficient of variation (CV)4.74904061 × 1015
Kurtosis0.01552711195
Mean2.105710618 × 10-16
Median Absolute Deviation (MAD)0.7077833581
Skewness-0.3064619664
Sum1.000444172 × 10-11
Variance1.000021048
MonotonicityNot monotonic
2021-09-09T16:20:49.185665image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5009593861247
 
2.6%
0.62964726931231
 
2.6%
0.56530332771213
 
2.6%
0.37227150271202
 
2.5%
0.6939912111194
 
2.5%
0.75833515261171
 
2.5%
0.43661544441166
 
2.5%
-0.3998557971156
 
2.4%
-0.52854368031150
 
2.4%
-0.46419973861134
 
2.4%
Other values (89)35647
75.0%
ValueCountFrequency (%)
-2.844925579658
1.4%
-2.78058163830
 
0.1%
-2.71623769650
 
0.1%
-2.65189375440
 
0.1%
-2.58754981341
 
0.1%
-2.52320587127
 
0.1%
-2.4588619313
 
< 0.1%
-2.39451798821
 
< 0.1%
-2.33017404643
 
0.1%
-2.26583010556
 
0.1%
ValueCountFrequency (%)
3.5251246431
 
< 0.1%
3.396436761
 
< 0.1%
3.3320928182
 
< 0.1%
3.2677488772
 
< 0.1%
3.2034049351
 
< 0.1%
3.1390609932
 
< 0.1%
3.0747170521
 
< 0.1%
3.010373112
 
< 0.1%
2.9460291686
< 0.1%
2.8816852277
< 0.1%

acousticness
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4162
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-9.571411902 × 10-18
Minimum-0.8971117969
Maximum2.02371963
Zeros0
Zeros (%)0.0%
Negative30054
Negative (%)63.3%
Memory size371.3 KiB
2021-09-09T16:20:49.374692image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-0.8971117969
5-th percentile-0.8959593001
Q1-0.8384605634
median-0.4748229159
Q30.7187296852
95-th percentile1.97093352
Maximum2.02371963
Range2.920831427
Interquartile range (IQR)1.557190249

Descriptive statistics

Standard deviation1.000010524
Coefficient of variation (CV)-1.044788934 × 1017
Kurtosis-0.7156543301
Mean-9.571411902 × 10-18
Median Absolute Deviation (MAD)0.4138431034
Skewness0.884777621
Sum-4.547473509 × 10-13
Variance1.000021048
MonotonicityNot monotonic
2021-09-09T16:20:49.556665image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.020787068260
 
0.5%
2.017854507229
 
0.5%
2.011989383208
 
0.4%
2.014921945189
 
0.4%
2.009056821151
 
0.3%
2.00612426144
 
0.3%
2.003191698126
 
0.3%
1.991461451112
 
0.2%
1.982663766110
 
0.2%
1.997326575107
 
0.2%
Other values (4152)45875
96.6%
ValueCountFrequency (%)
-0.89711179691
 
< 0.1%
-0.89710880571
 
< 0.1%
-0.89710807251
 
< 0.1%
-0.89710777931
 
< 0.1%
-0.89710774991
 
< 0.1%
-0.89710772063
< 0.1%
-0.89710751531
 
< 0.1%
-0.89710725141
 
< 0.1%
-0.89710719281
 
< 0.1%
-0.89710710481
 
< 0.1%
ValueCountFrequency (%)
2.0237196379
 
0.2%
2.020787068260
0.5%
2.017854507229
0.5%
2.014921945189
0.4%
2.011989383208
0.4%
2.009056821151
0.3%
2.00612426144
0.3%
2.003191698126
0.3%
2.00025913698
 
0.2%
1.997326575107
0.2%

danceability
Real number (ℝ)

HIGH CORRELATION

Distinct1083
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-4.785705951 × 10-17
Minimum-2.791973762
Maximum2.393419475
Zeros0
Zeros (%)0.0%
Negative22735
Negative (%)47.9%
Memory size371.3 KiB
2021-09-09T16:20:49.752696image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2.791973762
5-th percentile-1.821391738
Q1-0.6515437385
median0.05372347638
Q30.7198091793
95-th percentile1.559413007
Maximum2.393419475
Range5.185393237
Interquartile range (IQR)1.371352918

Descriptive statistics

Standard deviation1.000010524
Coefficient of variation (CV)-2.089577868 × 1016
Kurtosis-0.2989001137
Mean-4.785705951 × 10-17
Median Absolute Deviation (MAD)0.6828777795
Skewness-0.3014020766
Sum-2.273736754 × 10-12
Variance1.000021048
MonotonicityNot monotonic
2021-09-09T16:20:49.947759image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.1645735187133
 
0.3%
0.5518884139133
 
0.3%
-0.3324942841129
 
0.3%
-0.06382105943128
 
0.3%
0.288812548127
 
0.3%
0.1712680122126
 
0.3%
0.3503834953122
 
0.3%
0.3895650073121
 
0.3%
-0.0246395475121
 
0.3%
-0.2989101311120
 
0.3%
Other values (1073)46251
97.3%
ValueCountFrequency (%)
-2.7919737621
< 0.1%
-2.7897348181
< 0.1%
-2.7886153471
< 0.1%
-2.7863764032
< 0.1%
-2.7858166672
< 0.1%
-2.784137461
< 0.1%
-2.7824582521
< 0.1%
-2.7818985161
< 0.1%
-2.7807790441
< 0.1%
-2.7802193081
< 0.1%
ValueCountFrequency (%)
2.3934194751
 
< 0.1%
2.3598353222
< 0.1%
2.3542379631
 
< 0.1%
2.3486406042
< 0.1%
2.3430432452
< 0.1%
2.3374458861
 
< 0.1%
2.3262511691
 
< 0.1%
2.320653811
 
< 0.1%
2.3150564512
< 0.1%
2.3094590924
< 0.1%

duration_ms
Real number (ℝ)

Distinct25167
Distinct (%)53.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.823566511 × 10-16
Minimum-2.181305949
Maximum43.5020136
Zeros0
Zeros (%)0.0%
Negative30852
Negative (%)64.9%
Memory size371.3 KiB
2021-09-09T16:20:50.138696image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2.181305949
5-th percentile-1.061328166
Q1-0.4794776609
median-0.08958336416
Q30.2194820228
95-th percentile1.479334284
Maximum43.5020136
Range45.68331954
Interquartile range (IQR)0.6989596836

Descriptive statistics

Standard deviation1.000010524
Coefficient of variation (CV)-3.541657404 × 1015
Kurtosis215.2874484
Mean-2.823566511 × 10-16
Median Absolute Deviation (MAD)0.3593207699
Skewness7.934599935
Sum-1.341504685 × 10-11
Variance1.000021048
MonotonicityNot monotonic
2021-09-09T16:20:50.319664image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-2.761231158 × 10-164696
 
9.9%
-0.0514435417333
 
0.1%
-0.506844406628
 
0.1%
-0.620694622827
 
0.1%
-0.355044118319
 
< 0.1%
-0.279143974219
 
< 0.1%
-0.563769514718
 
< 0.1%
-0.567564521917
 
< 0.1%
-0.430944262416
 
< 0.1%
-0.260168938116
 
< 0.1%
Other values (25157)42622
89.7%
ValueCountFrequency (%)
-2.1813059491
< 0.1%
-2.1736495221
< 0.1%
-2.1416101741
< 0.1%
-2.1377867041
< 0.1%
-2.1126068311
< 0.1%
-2.1007474341
< 0.1%
-2.0789925551
< 0.1%
-2.0749698471
< 0.1%
-2.0538126821
< 0.1%
-2.0529303431
< 0.1%
ValueCountFrequency (%)
43.50201361
< 0.1%
40.346351251
< 0.1%
38.240179181
< 0.1%
27.988346711
< 0.1%
19.435918471
< 0.1%
16.829630851
< 0.1%
16.037366181
< 0.1%
15.438381211
< 0.1%
14.544533681
< 0.1%
13.016028111
< 0.1%

energy
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2065
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.627140023 × 10-16
Minimum-2.268510014
Maximum1.510167443
Zeros0
Zeros (%)0.0%
Negative21013
Negative (%)44.2%
Memory size371.3 KiB
2021-09-09T16:20:50.831663image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2.268510014
5-th percentile-1.994033808
Q1-0.6286180238
median0.1625433258
Q30.8174280793
95-th percentile1.336036237
Maximum1.510167443
Range3.778677457
Interquartile range (IQR)1.446046103

Descriptive statistics

Standard deviation1.000010524
Coefficient of variation (CV)-6.14581726 × 1015
Kurtosis-0.587293987
Mean-1.627140023 × 10-16
Median Absolute Deviation (MAD)0.7078812075
Skewness-0.5716370046
Sum-7.730704965 × 10-12
Variance1.000021048
MonotonicityNot monotonic
2021-09-09T16:20:51.016660image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.454023823196
 
0.2%
0.870424533495
 
0.2%
0.283678077994
 
0.2%
0.980202902594
 
0.2%
0.775788008393
 
0.2%
0.847711767489
 
0.2%
0.43131105788
 
0.2%
1.14297772688
 
0.2%
0.968846519587
 
0.2%
0.938562831486
 
0.2%
Other values (2055)46601
98.1%
ValueCountFrequency (%)
-2.2685100141
< 0.1%
-2.2683850941
< 0.1%
-2.2681011841
< 0.1%
-2.2679384091
< 0.1%
-2.2679005551
< 0.1%
-2.2676847831
< 0.1%
-2.267571221
< 0.1%
-2.267495512
< 0.1%
-2.2674198011
< 0.1%
-2.2673440921
< 0.1%
ValueCountFrequency (%)
1.5101674435
 
< 0.1%
1.50638198216
 
< 0.1%
1.50259652117
 
< 0.1%
1.4988110629
0.1%
1.49502559943
0.1%
1.49124013835
0.1%
1.48745467728
0.1%
1.48366921636
0.1%
1.47988375549
0.1%
1.47609829442
0.1%

instrumentalness
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION

Distinct5101
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.571411902 × 10-18
Minimum-0.5585759203
Maximum2.501600129
Zeros0
Zeros (%)0.0%
Negative35912
Negative (%)75.6%
Memory size371.3 KiB
2021-09-09T16:20:51.226659image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-0.5585759203
5-th percentile-0.5585759203
Q1-0.5585759203
median-0.5580873982
Q3-0.08080747074
95-th percentile2.231223129
Maximum2.501600129
Range3.06017605
Interquartile range (IQR)0.4777684495

Descriptive statistics

Standard deviation1.000010524
Coefficient of variation (CV)1.044788934 × 1017
Kurtosis0.4513913283
Mean9.571411902 × 10-18
Median Absolute Deviation (MAD)0.0004885220802
Skewness1.485120446
Sum4.547473509 × 10-13
Variance1.000021048
MonotonicityNot monotonic
2021-09-09T16:20:51.398695image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.558575920314248
30.0%
2.2004984767
 
0.1%
2.21278833466
 
0.1%
2.19742600462
 
0.1%
2.24351299362
 
0.1%
2.17899120961
 
0.1%
2.20664340259
 
0.1%
2.27731011859
 
0.1%
2.31110724358
 
0.1%
2.25887532257
 
0.1%
Other values (5091)32712
68.9%
ValueCountFrequency (%)
-0.558575920314248
30.0%
-0.55857284783
 
< 0.1%
-0.558572817125
 
0.1%
-0.558572786416
 
< 0.1%
-0.558572755619
 
< 0.1%
-0.558572724918
 
< 0.1%
-0.558572694218
 
< 0.1%
-0.558572663516
 
< 0.1%
-0.558572632718
 
< 0.1%
-0.55857260216
 
< 0.1%
ValueCountFrequency (%)
2.5016001291
 
< 0.1%
2.4954551981
 
< 0.1%
2.4923827322
 
< 0.1%
2.4893102662
 
< 0.1%
2.4800928681
 
< 0.1%
2.4770204023
< 0.1%
2.4739479363
< 0.1%
2.470875473
< 0.1%
2.4678030045
< 0.1%
2.4647305395
< 0.1%

key
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6.101775088 × 10-17
Minimum-1.548514114
Maximum1.632191426
Zeros0
Zeros (%)0.0%
Negative26742
Negative (%)56.3%
Memory size371.3 KiB
2021-09-09T16:20:51.562663image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1.548514114
5-th percentile-1.548514114
Q1-0.6810489666
median-0.1027388685
Q30.7647262786
95-th percentile1.632191426
Maximum1.632191426
Range3.18070554
Interquartile range (IQR)1.445775245

Descriptive statistics

Standard deviation1.000010524
Coefficient of variation (CV)-1.638884603 × 1016
Kurtosis-1.227188854
Mean-6.101775088 × 10-17
Median Absolute Deviation (MAD)0.8674651471
Skewness0.08268596361
Sum-2.899014362 × 10-12
Variance1.000021048
MonotonicityNot monotonic
2021-09-09T16:20:51.682693image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1.3430363775429
11.4%
-0.68104896665223
11.0%
-0.39189391765136
10.8%
-0.10273886854993
10.5%
-1.5485141144591
9.7%
0.76472627864139
8.7%
-0.97020401573607
7.6%
0.47557122963568
7.5%
-1.2593590653192
6.7%
1.6321914263164
6.7%
Other values (2)4469
9.4%
ValueCountFrequency (%)
-1.5485141144591
9.7%
-1.2593590653192
6.7%
-0.97020401573607
7.6%
-0.68104896665223
11.0%
-0.39189391765136
10.8%
-0.10273886854993
10.5%
0.18641618051517
 
3.2%
0.47557122963568
7.5%
0.76472627864139
8.7%
1.0538813282952
6.2%
ValueCountFrequency (%)
1.6321914263164
6.7%
1.3430363775429
11.4%
1.0538813282952
6.2%
0.76472627864139
8.7%
0.47557122963568
7.5%
0.18641618051517
 
3.2%
-0.10273886854993
10.5%
-0.39189391765136
10.8%
-0.68104896665223
11.0%
-0.97020401573607
7.6%

liveness
Real number (ℝ)

Distinct1639
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5.024991249 × 10-17
Minimum-1.14012787
Maximum4.988339897
Zeros0
Zeros (%)0.0%
Negative32363
Negative (%)68.1%
Memory size371.3 KiB
2021-09-09T16:20:51.849697image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1.14012787
5-th percentile-0.8144371826
Q1-0.6003217012
median-0.4202419177
Q30.3099785104
95-th percentile2.206695131
Maximum4.988339897
Range6.128467767
Interquartile range (IQR)0.9103002116

Descriptive statistics

Standard deviation1.000010524
Coefficient of variation (CV)-1.99007416 × 1016
Kurtosis5.708006136
Mean-5.024991249 × 10-17
Median Absolute Deviation (MAD)0.2741420759
Skewness2.248357453
Sum-2.387423592 × 10-12
Variance1.000021048
MonotonicityNot monotonic
2021-09-09T16:20:52.022661image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.5192548571598
 
1.3%
-0.5316314745580
 
1.2%
-0.5130665484579
 
1.2%
-0.5378197832520
 
1.1%
-0.5254431658516
 
1.1%
-0.5068782397497
 
1.0%
-0.5563847093485
 
1.0%
-0.5501964006467
 
1.0%
-0.5440080919461
 
1.0%
-0.5625730181459
 
1.0%
Other values (1629)42349
89.1%
ValueCountFrequency (%)
-1.140127871
< 0.1%
-1.1158078171
< 0.1%
-1.1028123691
< 0.1%
-1.0953863981
< 0.1%
-1.0929110751
< 0.1%
-1.0836286122
< 0.1%
-1.0817721191
< 0.1%
-1.0799156261
< 0.1%
-1.0786779651
< 0.1%
-1.0737273181
< 0.1%
ValueCountFrequency (%)
4.9883398972
< 0.1%
4.9635866621
 
< 0.1%
4.9450217361
 
< 0.1%
4.9388334271
 
< 0.1%
4.9326451182
< 0.1%
4.926456813
< 0.1%
4.9202685012
< 0.1%
4.9140801923
< 0.1%
4.9078918832
< 0.1%
4.9017035752
< 0.1%

loudness
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct16864
Distinct (%)35.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.339997666 × 10-16
Minimum-6.163531779
Maximum2.091670296
Zeros0
Zeros (%)0.0%
Negative16511
Negative (%)34.8%
Memory size371.3 KiB
2021-09-09T16:20:52.224660image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-6.163531779
5-th percentile-2.241132407
Q1-0.2798920875
median0.3005238725
Q30.6418494161
95-th percentile0.9848003194
Maximum2.091670296
Range8.255202075
Interquartile range (IQR)0.9217415036

Descriptive statistics

Standard deviation1.000010524
Coefficient of variation (CV)7.462778102 × 1015
Kurtosis4.020996997
Mean1.339997666 × 10-16
Median Absolute Deviation (MAD)0.4130039077
Skewness-1.873985086
Sum6.366462912 × 10-12
Variance1.000021048
MonotonicityNot monotonic
2021-09-09T16:20:52.393660image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.598452311318
 
< 0.1%
0.648838462917
 
< 0.1%
0.510845421716
 
< 0.1%
0.575047131116
 
< 0.1%
0.571958947616
 
< 0.1%
0.334656426916
 
< 0.1%
0.668342779716
 
< 0.1%
0.408610294615
 
< 0.1%
0.425188963914
 
< 0.1%
0.499305367614
 
< 0.1%
Other values (16854)47353
99.7%
ValueCountFrequency (%)
-6.1635317791
< 0.1%
-6.1536170841
< 0.1%
-6.0759248891
< 0.1%
-6.0133485391
< 0.1%
-5.734436811
< 0.1%
-5.686001091
< 0.1%
-5.6759238591
< 0.1%
-5.5720633721
< 0.1%
-5.5278535881
< 0.1%
-5.5099746311
< 0.1%
ValueCountFrequency (%)
2.0916702961
< 0.1%
1.7999182251
< 0.1%
1.790816211
< 0.1%
1.744818531
< 0.1%
1.740755131
< 0.1%
1.7012588891
< 0.1%
1.6967078821
< 0.1%
1.6903689791
< 0.1%
1.6494099131
< 0.1%
1.6476220181
< 0.1%

mode
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size371.3 KiB
-0.747331373879606
30485 
1.3380944985739334
17026 

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters855198
Distinct characters11
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.747331373879606
2nd row1.3380944985739334
3rd row-0.747331373879606
4th row-0.747331373879606
5th row1.3380944985739334

Common Values

ValueCountFrequency (%)
-0.74733137387960630485
64.2%
1.338094498573933417026
35.8%

Length

2021-09-09T16:20:52.725665image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T16:20:52.822661image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.74733137387960630485
64.2%
1.338094498573933417026
35.8%

Most occurring characters

ValueCountFrequency (%)
3207070
24.2%
7138966
16.2%
481563
 
9.5%
981563
 
9.5%
077996
 
9.1%
864537
 
7.5%
660970
 
7.1%
.47511
 
5.6%
147511
 
5.6%
-30485
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number777202
90.9%
Other Punctuation47511
 
5.6%
Dash Punctuation30485
 
3.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3207070
26.6%
7138966
17.9%
481563
 
10.5%
981563
 
10.5%
077996
 
10.0%
864537
 
8.3%
660970
 
7.8%
147511
 
6.1%
517026
 
2.2%
Dash Punctuation
ValueCountFrequency (%)
-30485
100.0%
Other Punctuation
ValueCountFrequency (%)
.47511
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common855198
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3207070
24.2%
7138966
16.2%
481563
 
9.5%
981563
 
9.5%
077996
 
9.1%
864537
 
7.5%
660970
 
7.1%
.47511
 
5.6%
147511
 
5.6%
-30485
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII855198
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3207070
24.2%
7138966
16.2%
481563
 
9.5%
981563
 
9.5%
077996
 
9.1%
864537
 
7.5%
660970
 
7.1%
.47511
 
5.6%
147511
 
5.6%
-30485
 
3.6%

speechiness
Real number (ℝ)

HIGH CORRELATION

Distinct1332
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.244283547 × 10-16
Minimum-0.7043794893
Maximum8.391631955
Zeros0
Zeros (%)0.0%
Negative35106
Negative (%)73.9%
Memory size371.3 KiB
2021-09-09T16:20:52.951661image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-0.7043794893
5-th percentile-0.647016364
Q1-0.5678948118
median-0.4413003283
Q30.05024231471
95-th percentile2.25971166
Maximum8.391631955
Range9.096011444
Interquartile range (IQR)0.6181371265

Descriptive statistics

Standard deviation1.000010524
Coefficient of variation (CV)-8.036837956 × 1015
Kurtosis7.393061602
Mean-1.244283547 × 10-16
Median Absolute Deviation (MAD)0.1691223178
Skewness2.46969695
Sum-5.911715562 × 10-12
Variance1.000021048
MonotonicityNot monotonic
2021-09-09T16:20:53.136664image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.5965763745163
 
0.3%
-0.5916312775149
 
0.3%
-0.6133897043144
 
0.3%
-0.565916773143
 
0.3%
-0.5995434327141
 
0.3%
-0.5926202969141
 
0.3%
-0.57086187140
 
0.3%
-0.5936093163140
 
0.3%
-0.5787740252139
 
0.3%
-0.5669057924138
 
0.3%
Other values (1322)46073
97.0%
ValueCountFrequency (%)
-0.70437948931
 
< 0.1%
-0.70339046993
 
< 0.1%
-0.70240145051
 
< 0.1%
-0.70141243112
 
< 0.1%
-0.70042341171
 
< 0.1%
-0.69943439236
< 0.1%
-0.69844537293
 
< 0.1%
-0.69745635352
 
< 0.1%
-0.69646733418
< 0.1%
-0.69547831474
< 0.1%
ValueCountFrequency (%)
8.3916319551
 
< 0.1%
8.3817417611
 
< 0.1%
8.3619613731
 
< 0.1%
8.2927300141
 
< 0.1%
8.2432790441
 
< 0.1%
8.1938280743
< 0.1%
8.1740476861
 
< 0.1%
8.1542672982
< 0.1%
8.0158045821
 
< 0.1%
7.8674516711
 
< 0.1%

tempo
Real number (ℝ)

Distinct28373
Distinct (%)59.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-7.896414819 × 10-17
Minimum-2.94172616
Maximum3.446892983
Zeros0
Zeros (%)0.0%
Negative26234
Negative (%)55.2%
Memory size371.3 KiB
2021-09-09T16:20:53.324661image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2.94172616
5-th percentile-1.490852744
Q1-0.8016148555
median-7.812680192 × 10-15
Q30.6731563094
95-th percentile1.829783319
Maximum3.446892983
Range6.388619143
Interquartile range (IQR)1.474771165

Descriptive statistics

Standard deviation1.000010524
Coefficient of variation (CV)-1.266410829 × 1016
Kurtosis-0.2943275346
Mean-7.896414819 × 10-17
Median Absolute Deviation (MAD)0.7052662254
Skewness0.3404009686
Sum-3.751665645 × 10-12
Variance1.000021048
MonotonicityNot monotonic
2021-09-09T16:20:53.502664image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-7.812680192 × 10-154720
 
9.9%
0.00135639020616
 
< 0.1%
0.688807531216
 
< 0.1%
-0.685751145416
 
< 0.1%
0.345511467315
 
< 0.1%
0.00200924029314
 
< 0.1%
-0.857450718214
 
< 0.1%
-0.68578550614
 
< 0.1%
0.688944973313
 
< 0.1%
0.000840982243513
 
< 0.1%
Other values (28363)42660
89.8%
ValueCountFrequency (%)
-2.941726161
< 0.1%
-2.9375341751
< 0.1%
-2.9273634581
< 0.1%
-2.924236651
< 0.1%
-2.9003560811
< 0.1%
-2.8466505711
< 0.1%
-2.8219453491
< 0.1%
-2.8114997481
< 0.1%
-2.7416104281
< 0.1%
-2.6033780121
< 0.1%
ValueCountFrequency (%)
3.4468929831
< 0.1%
3.4388182581
< 0.1%
3.4144222811
< 0.1%
3.3697192311
< 0.1%
3.3661800961
< 0.1%
3.3528138491
< 0.1%
3.3009638081
< 0.1%
3.2971497891
< 0.1%
3.2779422531
< 0.1%
3.2239618592
< 0.1%

obtained_date
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size371.3 KiB
0.23344849246835683
42518 
-2.6207386731683058
 
3863
3.0876356581050195
 
746
-5.474925838804968
 
383
-8.32911300444163
 
1

Length

Max length19
Median length19
Mean length18.97619499
Min length17

Characters and Unicode

Total characters901578
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.23344849246835683
2nd row0.23344849246835683
3rd row0.23344849246835683
4th row0.23344849246835683
5th row0.23344849246835683

Common Values

ValueCountFrequency (%)
0.2334484924683568342518
89.5%
-2.62073867316830583863
 
8.1%
3.0876356581050195746
 
1.6%
-5.474925838804968383
 
0.8%
-8.329113004441631
 
< 0.1%

Length

2021-09-09T16:20:53.871660image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T16:20:54.030658image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.2334484924683568342518
89.5%
2.62073867316830583863
 
8.1%
3.0876356581050195746
 
1.6%
5.474925838804968383
 
0.8%
8.329113004441631
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
3183539
20.4%
4171224
19.0%
8142168
15.8%
698501
10.9%
293146
10.3%
052867
 
5.9%
550131
 
5.6%
.47511
 
5.3%
944031
 
4.9%
78855
 
1.0%
Other values (2)9605
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number849820
94.3%
Other Punctuation47511
 
5.3%
Dash Punctuation4247
 
0.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3183539
21.6%
4171224
20.1%
8142168
16.7%
698501
11.6%
293146
11.0%
052867
 
6.2%
550131
 
5.9%
944031
 
5.2%
78855
 
1.0%
15358
 
0.6%
Other Punctuation
ValueCountFrequency (%)
.47511
100.0%
Dash Punctuation
ValueCountFrequency (%)
-4247
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common901578
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3183539
20.4%
4171224
19.0%
8142168
15.8%
698501
10.9%
293146
10.3%
052867
 
5.9%
550131
 
5.6%
.47511
 
5.3%
944031
 
4.9%
78855
 
1.0%
Other values (2)9605
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII901578
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3183539
20.4%
4171224
19.0%
8142168
15.8%
698501
10.9%
293146
10.3%
052867
 
5.9%
550131
 
5.6%
.47511
 
5.3%
944031
 
4.9%
78855
 
1.0%
Other values (2)9605
 
1.1%

valence
Real number (ℝ)

HIGH CORRELATION

Distinct1612
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.770711202 × 10-16
Minimum-1.846442493
Maximum2.167710127
Zeros0
Zeros (%)0.0%
Negative24267
Negative (%)51.1%
Memory size371.3 KiB
2021-09-09T16:20:54.274662image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1.846442493
5-th percentile-1.576134635
Q1-0.8064856149
median-0.03359937442
Q30.7757055894
95-th percentile1.702359773
Maximum2.167710127
Range4.014152621
Interquartile range (IQR)1.582191204

Descriptive statistics

Standard deviation1.000010524
Coefficient of variation (CV)-5.647507753 × 1015
Kurtosis-0.9321729154
Mean-1.770711202 × 10-16
Median Absolute Deviation (MAD)0.7890723397
Skewness0.1328855542
Sum-8.412825991 × 10-12
Variance1.000021048
MonotonicityNot monotonic
2021-09-09T16:20:54.490660image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.478717104595
 
0.2%
-0.53536845292
 
0.2%
-0.502996253489
 
0.2%
-0.426112281988
 
0.2%
-0.397786608183
 
0.2%
-0.341135260781
 
0.2%
2.04226785881
 
0.2%
-0.349228310379
 
0.2%
-0.442298381279
 
0.2%
0.201099065178
 
0.2%
Other values (1602)46666
98.2%
ValueCountFrequency (%)
-1.8464424932
< 0.1%
-1.7683445641
< 0.1%
-1.7634887351
< 0.1%
-1.7517538131
< 0.1%
-1.7489212451
< 0.1%
-1.7468979831
< 0.1%
-1.746493331
< 0.1%
-1.744874721
< 0.1%
-1.7444700681
< 0.1%
-1.7416375011
< 0.1%
ValueCountFrequency (%)
2.1677101271
 
< 0.1%
2.1596170781
 
< 0.1%
2.1555705531
 
< 0.1%
2.1474775031
 
< 0.1%
2.1434309781
 
< 0.1%
2.1393844533
< 0.1%
2.1353379291
 
< 0.1%
2.1312914041
 
< 0.1%
2.1272448792
< 0.1%
2.1231983541
 
< 0.1%

music_genre
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size371.3 KiB
2.0
9556 
3.0
9525 
4.0
9495 
1.0
9479 
0.0
9456 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters142533
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
2.09556
20.1%
3.09525
20.0%
4.09495
20.0%
1.09479
20.0%
0.09456
19.9%

Length

2021-09-09T16:20:54.872661image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T16:20:54.980659image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
2.09556
20.1%
3.09525
20.0%
4.09495
20.0%
1.09479
20.0%
0.09456
19.9%

Most occurring characters

ValueCountFrequency (%)
056967
40.0%
.47511
33.3%
29556
 
6.7%
39525
 
6.7%
49495
 
6.7%
19479
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number95022
66.7%
Other Punctuation47511
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
056967
60.0%
29556
 
10.1%
39525
 
10.0%
49495
 
10.0%
19479
 
10.0%
Other Punctuation
ValueCountFrequency (%)
.47511
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common142533
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
056967
40.0%
.47511
33.3%
29556
 
6.7%
39525
 
6.7%
49495
 
6.7%
19479
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII142533
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
056967
40.0%
.47511
33.3%
29556
 
6.7%
39525
 
6.7%
49495
 
6.7%
19479
 
6.7%

Interactions

2021-09-09T16:19:49.264859image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:19:49.738853image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:19:50.055859image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:19:50.363856image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:19:50.637851image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:19:50.864851image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:19:51.053886image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:19:51.246854image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:19:51.473861image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:19:51.695853image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:19:51.910853image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:19:52.115856image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:19:52.317851image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:19:52.489529image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:19:52.668566image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:19:52.895530image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:19:53.097564image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:19:53.293531image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:19:53.489529image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:19:53.692553image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:19:53.892534image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:19:54.105535image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:19:54.343531image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:19:54.550535image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:19:54.744565image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:19:54.953154image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:19:55.200530image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:19:55.439532image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:19:55.630567image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:19:55.913351image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:19:56.094388image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:19:56.285384image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:19:56.488379image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:19:56.680355image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:19:56.875388image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:19:57.069383image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:19:57.267387image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:19:57.459387image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:19:57.655354image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:19:57.847473image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:19:58.032508image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:19:58.269505image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:19:58.528471image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:19:58.781469image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:19:58.978507image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:19:59.253470image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:19:59.434472image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:19:59.622473image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:19:59.905477image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:00.098468image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:00.292504image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:00.513474image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:00.741471image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:00.937503image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:01.133505image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:01.324505image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:01.519506image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:01.859475image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:02.164473image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:02.364503image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:02.560503image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:02.761506image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:02.959472image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:03.281708image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:03.545707image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:03.831701image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:04.061913image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:04.286707image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:04.506737image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:04.723742image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:04.980704image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:05.188706image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:05.385707image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:05.604707image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:05.840710image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:06.084741image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:06.338702image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:06.641700image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:06.887708image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:07.151707image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:07.388701image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:07.578705image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:07.810703image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:08.031705image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:08.261705image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:08.472703image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:08.676706image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:08.868706image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:09.054707image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:09.257706image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:09.478738image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:09.675499image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:09.875528image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:10.084497image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:10.280496image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:10.482530image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:10.715496image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:10.906498image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:11.102531image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:11.301504image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:11.488531image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:11.675534image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:11.904502image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:12.146498image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:12.360492image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:12.736495image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:12.925396image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:13.111401image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:13.300368image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:13.476413image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:13.655367image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:13.844400image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:14.046370image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:14.226884image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:14.412913image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:14.605883image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:14.791883image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:14.974919image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:15.162916image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:15.342918image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:15.507346image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:15.694312image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:15.928315image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:16.202351image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T16:20:16.383314image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-09-09T16:20:45.972513image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-09-09T16:20:55.157664image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-09-09T16:20:55.570659image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-09-09T16:20:55.970696image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-09-09T16:20:56.379658image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-09-09T16:20:56.725664image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-09-09T16:20:46.391547image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-09-09T16:20:46.957512image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexartist_nametrack_hashtrack_namepopularityacousticnessdanceabilityduration_msenergyinstrumentalnesskeylivenessloudnessmodespeechinesstempoobtained_datevalencemusic_genre
00-1.489739-1.2584200.5702480.307928-0.896807-0.1477811.532307e-011.336036-0.5350721.632191-0.4635600.562044-0.747331-0.5352577.578378e-010.2334480.1161220.0
11-1.475657-1.274109-0.3314320.3722720.363890-0.3548841.549090e-02-0.253857-0.556511-0.681049-0.5440080.5401021.338094-0.633170-1.383029e+000.233448-0.1307160.0
221.081735-1.272096-0.8547320.243584-0.890777-0.472428-1.069141e+001.491240-0.1714451.6321910.7431601.176918-0.7473310.0937591.768622e+000.2334481.5809640.0
33-0.308863-1.5424291.2166150.243584-0.577463-1.2000853.227442e-010.253394-0.5585761.053881-0.5625730.555868-0.747331-0.544158-1.466148e+000.233448-1.0047650.0
441.364381-1.267649-0.2948960.372272-0.888167-1.3288243.698013e-020.832570-0.558576-0.6810490.9040560.7583881.338094-0.507565-7.812680e-150.233448-1.0452310.0
55-0.882201-1.175104-0.176461-0.142480-0.046669-0.774686-2.761231e-160.847712-0.558576-1.259359-0.4759370.8811021.3380940.726732-1.382583e+000.2334480.0068660.0
660.584338-1.419512-1.510189-0.206824-0.895748-1.614289-1.039380e-010.628155-0.558556-1.548514-0.6591110.637623-0.747331-0.5352571.648085e+000.233448-1.0816490.0
77-1.593342-1.437551-0.2899140.179240-0.1669040.417552-9.769319e-01-0.564265-0.5515400.186416-0.5130670.6444501.338094-0.3285521.203391e+000.233448-1.3203940.0
891.387012-1.164365-0.841446-0.142480-0.559867-0.640349-4.083165e-011.082410-0.556462-0.681049-0.1850860.555868-0.747331-0.593609-7.812680e-150.2334481.1318000.0
910-0.710703-1.5400801.2602310.565303-0.084792-0.1477811.098330e+00-0.238716-0.558550-0.681049-0.0180020.363425-0.747331-0.6371266.884639e-010.233448-1.2232780.0

Last rows

df_indexartist_nametrack_hashtrack_namepopularityacousticnessdanceabilityduration_msenergyinstrumentalnesskeylivenessloudnessmodespeechinesstempoobtained_datevalencemusic_genre
47501499901.6726761.0263280.1517480.887023-0.8525370.098502-0.3948921.267898-0.558561-1.5485140.9597510.839006-0.747331-0.416575-0.4865290.2334481.1358464.0
47502499911.2637951.257395-1.2805741.9808700.135150-0.035834-0.7879310.060336-0.558576-0.391894-0.889935-0.1072791.338094-0.582730-0.2583410.2334480.8525904.0
47503499920.7005151.126675-1.0800642.109558-0.847258-0.0302370.3565201.002916-0.558544-1.5485140.4523100.7109271.338094-0.520422-0.8177640.2334481.1277534.0
47504499931.1642151.087325-0.4003080.4366150.3638900.216047-0.9913430.488093-0.557713-1.548514-0.1665210.938640-0.747331-0.6015210.0043110.2334481.0346834.0
47505499941.3543221.1188721.4145900.436615-0.658401-1.754223-0.0982450.435097-0.5585761.3430360.9102440.476713-0.747331-0.557016-1.5439050.233448-0.3613684.0
47506499951.1591861.649304-1.0692261.530462-0.5452040.596667-0.7250570.688722-0.5585631.053881-0.4264300.1116571.338094-0.6213020.6010510.2334481.1115674.0
47507499961.1591861.558521-0.4485561.208743-0.6320080.031334-1.0063910.859068-0.5585641.343036-0.4140540.330918-0.747331-0.645038-1.0347170.2334481.8439884.0
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